Calibrating Ambiguity Set via Diagnostic Transport for Distributionally Robust Optimization
Read the original on arXiv Machine Learning →The paper introduces Diagnostic-Transport DRO (DT‑DRO), a method that calibrates the geometry of the ambiguity set in distributionally robust optimization using held‑out data. By applying the conditional probability integral transform to detect systematic probability misallocation, DT‑DRO adjusts both the ambiguity‑set center and ground cost through an outcome‑level transport, yielding a computationally tractable dual reformulation. The authors provide theoretical guarantees on ambiguity radii and decision‑risk bounds that improve as estimation errors shrink, and demonstrate that DT‑DRO can remove the robustness floor caused by model misspecification in synthetic and power‑outage experiments.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.